Erik Hemberg

String-Rewriting Grammars for Evolutionary Architectural Design

Evolutionary methods afford a productive and creative alternative design workflow. Crucial to success is the choice of formal representation of the problem. String-rewriting context-free grammars (CFGs) are one common option in evolutionary computation, but their suitability for design is not obvious. Here, a CFG-based evolutionary algorithm for design is presented. The process of meta-design is described, in which the CFG is created and then refined to produce an improved design language. CFGs are contrasted with another grammatical formalism better known in architectural design: Stinys shape grammars. The advantages and disadvantages of the two types of grammars for design tasks are discussed.

PonyGE2: grammatical evolution in Python

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCDs Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.

Differential gene expression with tree-adjunct grammars

A novel extension of an existing artificial Gene Regulatory Network model is introduced, combining the dynamic adaptive nature of this model with the generative power of grammars. The use of grammars enables the model to produce more varied phenotypes, allowing its application to a wider range of problems. The performance and generalisation ability of the model on the inverted-pendulum problem, using a range of different grammars, is compared against the existing model.

Discrete Planar Truss Optimization by Node Position Variation Using Grammatical Evolution

The majority of existing discrete truss optimization methods focus primarily on optimizing global truss topology using a ground structure approach, in which all possible node and beam locations are specified a priori. The ground structure discrete optimization method has been shown to be restrictive as it limits derivable solutions to what is explicitly defined. Greater representational freedom can improve performance. In this paper, grammatical evolution is applied. It can represent a variable number of nodes and their locations on a continuum. A novel method of connecting evolved nodes using a Delaunay triangulation algorithm shows that fully triangulated, kinematically stable structures can be generated. Discrete beam-truss structures can be optimized without the need for any information about the desired form of the solution other than the design envelope. Our technique is compared to existing discrete optimization techniques, and notable savings in structure self-weight are demonstrated. In particular, our new method can produce results superior to those reported in the literature in cases in which the problem is ill-defined and the structure of the solution is not known a priori.

Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood

We detect tax law abuse by simulating the co-evolution of tax evasion schemes and their discovery through audits. Tax evasion accounts for billions of dollars of lost income each year. When the IRS pursues a tax evasion scheme and changes the tax law or audit procedures, the tax evasion schemes evolve and change into undetectable forms. The arms race between tax evasion schemes and tax authorities presents a serious compliance challenge. Tax evasion schemes are sequences of transactions where each transaction is individually compliant. However, when all transactions are combined they have no other purpose than to evade tax and are thus non-compliant. Our method consists of an ownership network and a sequence of transactions, which outputs the likelihood of conducting an audit, and requires no prior tax return or audit data. We adjust audit procedures for a new generation of evolved tax evasion schemes by simulating the gradual change of tax evasion schemes and audit points, i.e. methods used for detecting non-compliance. Additionally, we identify, for a given audit scoring procedure, which tax evasion schemes will likely escape auditing. The approach is demonstrated in the context of partnership tax law and the Installment Bogus Optional Basis tax evasion scheme. The experiments show the oscillatory behavior of a co-adapting system and that it can model the co-evolution of tax evasion schemes and their detection.

Understanding expansion order and phenotypic connectivity in πGE

Since its inception, πGE has used evolution to guide the order of how to construct derivation trees. It was hypothesised that this would allow evolution to adjust the order of expansion during the run and thus help with search. This research aims to identify if a specific order is reachable, how reachable it may be, and goes on to investigate what happens to the expansion order during a πGE run. It is concluded that within πGE we do not evolve towards a specific order but a rather distribution of orders. The added complexity that an evolvable order gives πGE can make it difficult to understand how it can effectively search, by examining the connectivity of the phenotypic landscape it is hoped to understand this. It is concluded that the addition of an evolvable derivation tree expansion order makes the phenotypic landscape associated with πGE very densely connected, with solutions now linked via a single mutation event that were not previously connected.

Imprecise selection and fitness approximation in a large-scale evolutionary rule based system for blood pressure prediction

We present how we have strategically allocated fitness evaluations in a large-scale rule based evolutionary system called ECStar. We describe a strategy that culls potentially weaker solutions early, then later only compete with solutions which have equivalent fitness evaluations, as they are evaluated on more fitness cases. Despite incurring some imprecision in fitness comparison, which arises from not evaluating on all the fitness cases or even the same ones, the strategy allows our system to make effective progress when the resources at its disposal are unpredictably available.

cCube: a cloud microservices architecture for evolutionary machine learning classification

We present cCube, a microservices open source architecture used to automatically create an application of one or more Evolutionary Machine Learning (EML) classification algorithms that can be deployed to the cloud with automatic data factorization, training, result filtering and fusion.

Efficient training set use for blood pressure prediction in a large scale learning classifier system

We define a machine learning problem to forecast arterial blood pressure. Our goal is to solve this problem with a large scale learning classifier system. Because learning classifiers systems are extremely computationally intensive and this problems eventually large training set will be very costly to execute, we address how to use less of the training set while not negatively impacting learning accuracy. Our approach is to allow competition among solutions which have not been evaluated on the entire training set. The best of these solutions are then evaluated on more of the training set while their offspring start off being evaluated on less of the training set. To keep selection fair, we divide competing solutions according to how many training examples they have been tested on.

Introducing graphical models to analyze genetic programming dynamics

We propose graphical models as a new means of understanding genetic programming dynamics. Herein, we describe how to build an unbiased graphical model from a population of genetic programming trees. Graphical models both express information about the conditional dependency relations among a set of random variables and they support probabilistic inference regarding the likelihood of a random variables outcome. We focus on the former information: by their structure, graphical models reveal structural dependencies between the nodes of genetic programming trees. We identify graphical model properties of potential interest in this regard - edge quantity and dependency among nodes expressed in terms of family relations. Using a simple symbolic regression problem we generate a graphical model of the population each generation. Then we interpret the graphical models with respect to conventional knowledge about the influence of subtree crossover and mutation upon tree structure.